Multi-adversarial Variational Autoencoder Networks

The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform unsupervised clustering or semi-supervised classification of images. Combining the power of these two generative models, we introduce a novel network architecture,...

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Veröffentlicht in:2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA) S. 777 - 782
Hauptverfasser: Imran, Abdullah-Al-Zubaer, Terzopoulos, Demetri
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2019
Schlagworte:
Computational modeling
Data models
Deep-generative-models
Gallium nitride
GANs
Generators
Image generation
image-classification
semi-supervised-learning
Supervised learning
Training
VAEs
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Zusammenfassung:The unsupervised training of GANs and VAEs has enabled them to generate realistic images mimicking real-world distributions and perform unsupervised clustering or semi-supervised classification of images. Combining the power of these two generative models, we introduce a novel network architecture, Multi-Adversarial Variational autoEncoder Networks (MAVENs), which incorporate an ensemble of discriminators in a combined VAE-GAN network, with simultaneous adversarial learning and variational inference. We apply MAVENs to the generation of synthetic images and propose a new distribution measure to evaluate the quality of the generated images. Our experimental results using the computer vision datasets SVHN and CIFAR-10 demonstrate competitive performance against state-of-the-art semi-supervised models both in image generation and classification tasks.
DOI:10.1109/ICMLA.2019.00137